Why Even More Clinical Research Studies May Be False: Effect of Asymmetrical Handling of Clinically Unexpected Values

PLoS ONE

Volumn 8, Issue 6, 2013, Pages

  Shun Shin, Matthew James ^a   Francis, Darrel P ^a  

^a NATIONAL HEART AND LUNG INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLINICAL RESEARCH; CONTROLLED STUDY; HUMAN; MEASUREMENT ERROR; MEDICAL PRACTICE; PATIENT CODING; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL; TADPOLE TEST; VALIDITY; ETHICS; MEDICAL RESEARCH; STANDARDS;

BIOMEDICAL RESEARCH; HUMANS;

EID: 84879386817     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0065323     Document Type: Article

References (15)

1. Hodge V, Austin J, (2004) A Survey of Outlier Detection Methodologies. Artificial Intelligence Review 22: 85-126 doi:10.1023/B:AIRE.0000045502.10941.a9.
2. D'Agostino RB, (1970) Transformation to Normality of the Null Distribution of G1. Biometrika pp. 679-681.
3. Wilkinson L, (1999) Dot Plots. The American Statistician 53: 276-281.
4. Committee for Practice Guidelines of the European Society of Cardiology (2010) Recommendations for Guidelines Production. European Society of Cardiology pp. 1-50 Available: http://www.escardio.org/guidelines-surveys/esc-guidelines/Documents/ESC%20Guidelines%20for%20Guidelines%20Update%202010.pdf. Accessed 2013 May 6.
5. Ioannidis JPA, (2005) Why most published research findings are false. PLoS Med 2: e124 doi:10.1371/journal.pmed.0020124.
6. Huwaldt JA. Plot Digitizer: GPL. Available: http://plotdigitizer.sourceforge.net/. Accessed 2013 May 6.
7. The Document Foundation (2013) Libreoffice 4.0.2. Available: http://www.libreoffice.org/. Accessed 2013 May 6.
8. R Development Core Team (2008) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available: http://www.R-project.org. Accessed 2013 May 6.
9. Kunz R, Oxman AD, (1998) The unpredictability paradox: review of empirical comparisons of randomised and non-randomised clinical trials. BMJ 317: 1185-1190.
10. Ioannidis JP, Haidich AB, Pappa M, Pantazis N, Kokori SI, et al. (2001) Comparison of evidence of treatment effects in randomized and nonrandomized studies. JAMA 286: 821-830.
11. Deeks JJ, Dinnes J, D'Amico R, Sowden AJ, Sakarovitch C, et al. (2003) Evaluating non-randomised intervention studies. Health Technol Assess 7: iii-173, iii-x, 1-173.
12. Elm von E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, et al. (2007) Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 335: 806-808 doi:10.1136/bmj.39335.541782.AD.
13. Schulz KF, Chalmers I, Hayes RJ, Altman DG, (1995) Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 273: 408-412.
14. Tzoulaki I, Siontis KCM, Ioannidis JPA, (2011) Prognostic effect size of cardiovascular biomarkers in datasets from observational studies versus randomised trials: meta-epidemiology study. BMJ 343: d6829.
15. Wilmshurst P, (2007) Dishonesty in medical research. Med Leg J 75: 3-12.